The bivariateLeaflet package provides tools for creating
interactive bivariate choropleth maps using Leaflet. Bivariate
choropleth maps allow you to visualize the relationship between two
variables simultaneously across geographic regions.
The bivariateLeaflet package addresses a need in
geospatial data visualization by providing a tool to create interactive
bivariate choropleth maps using Leaflet. These maps, which enable the
simultaneous visualization of relationships between two variables across
geographic regions, are powerful tools for analyzing complex datasets.
Despite their potential, creating bivariate maps has historically been a
challenging task, requiring both technical expertise and substantial
time investment.
This package simplifies the process, making bivariate mapping more
accessible to users of R. It is particularly valuable for researchers
and analysts working in fields such as demography, justice,
environmental studies, and public health. For instance,
bivariateLeaflet allows users to explore the relationship
between income and education, analyze correlations between temperature
and precipitation, or visualize connections between healthcare access
and health outcomes. By integrating seamlessly with the spatial data
format, sf, and offering an intuitive interface, the
package enables users to generate interactive maps that are both
visually compelling and informative.
The package’s functionality includes the ability to create bivariate maps with customizable color schemes, handle data challenges such as missing values and outliers, and provide clear, interpretable legends for effective communication. Through its integration with Leaflet, it ensures that the resulting visualizations are interactive and web-ready, enabling users to share insights widely.
You can install the package from CRAN:
Or install the development version from GitHub:
The package works with spatial data from various sources. We’ll demonstrate using census data at different geographic levels.
library(tidycensus)
library(tidyr)
library(dplyr)
library(sf)
# Get census API key if you haven't already
# census_api_key("YOUR_KEY_HERE")
# Get ACS data for DC census tracts
tract_data <- get_acs(
geography = "tract",
variables = c(
"B01003_001", # Total population
"B19013_001" # Median household income
),
state = "DC",
year = 2020,
geometry = TRUE
)
# Pivot data to wide format
tract_data_wide <- tract_data %>%
select(-moe) %>%
pivot_wider(
names_from = variable,
values_from = estimate
)# Get county-level data for entire US
county_data <- get_acs(
geography = "county",
variables = c(
"B01003_001", # Total population
"B19013_001" # Median household income
),
year = 2020,
geometry = TRUE
)
county_data_wide <- county_data %>%
select(-moe) %>%
pivot_wider(
names_from = variable,
values_from = estimate
)# Get block group data for DC
blockgroup_data <- get_acs(
geography = "block group",
variables = c(
"B01003_001", # Total population
"B19013_001" # Median household income
),
state = "DC",
year = 2020,
geometry = TRUE
)
blockgroup_data_wide <- blockgroup_data %>%
select(-moe) %>%
pivot_wider(
names_from = variable,
values_from = estimate
)Let’s start with creating a basic bivariate choropleth map using the DC census tract data:
# Create basic map
tract_map <- create_bivariate_map(
data = tract_data_wide,
var_1 = "B01003_001", # Total population
var_2 = "B19013_001" # Median household income
)
# Display the map
tract_mapThe default color scheme uses a 3x3 matrix where:
sequential_colors <- matrix(c(
"#49006a", "#2d004b", "#1a0027",
"#8c96c6", "#8856a7", "#810f7c",
"#edf8fb", "#bfd3e6", "#9ebcda"
), nrow = 3, byrow = TRUE)
sequential_map <- create_bivariate_map(
data = tract_data_wide,
var_1 = "B01003_001",
var_2 = "B19013_001",
color_matrix = sequential_colors
)
sequential_mapCensus data often includes missing values (NA) for various reasons. Here’s how to handle them:
# Identify tracts with missing data
missing_data <- tract_data_wide %>%
mutate(
missing_pop = is.na(B01003_001),
missing_income = is.na(B19013_001)
)
# Create a map excluding missing data
clean_map <- create_bivariate_map(
data = missing_data %>% filter(!missing_pop & !missing_income),
var_1 = "B01003_001",
var_2 = "B19013_001"
)
clean_mapYou can create custom tooltips for your map by providing your own labels:
# Create custom labels with tract names and formatted values
custom_labels <- sprintf(
"<strong>Census Tract:</strong> %s<br/>
<strong>Population:</strong> %s<br/>
<strong>Median Income:</strong> $%s",
tract_data_wide$NAME,
format(tract_data_wide$B01003_001, big.mark = ","),
format(tract_data_wide$B19013_001, big.mark = ",")
)
# Create map with custom labels
map_custom_labels <- create_bivariate_map(
data = tract_data_wide,
var_1 = "B01003_001",
var_2 = "B19013_001",
custom_labels = custom_labels
)
map_custom_labelsThis will create tooltips that show:
When creating bivariate choropleth maps:
The package includes various checks and warnings:
# Missing variable
try(calculate_tertiles(data.frame(x = 1:5), "nonexistent", "also_nonexistent"))
#> Error in calculate_tertiles(data.frame(x = 1:5), "nonexistent", "also_nonexistent") :
#> One or both of the specified variables are not in the dataset.
# Too few unique values
test_data <- data.frame(
var1 = c(1,1,1,2),
var2 = c(1,2,3,4)
)
calculate_tertiles(test_data, "var1", "var2")
#> var1 var2 var_1_tertile var_2_tertile
#> 1 1 1 1 1
#> 2 1 2 1 1
#> 3 1 3 2 2
#> 4 2 4 3 3# Identify outliers using IQR method
outlier_data <- tract_data_wide %>%
mutate(
pop_outlier = B01003_001 > quantile(B01003_001, 0.75, na.rm = TRUE) +
1.5 * IQR(B01003_001, na.rm = TRUE),
income_outlier = B19013_001 > quantile(B19013_001, 0.75, na.rm = TRUE) +
1.5 * IQR(B19013_001, na.rm = TRUE)
)
# Create map excluding outliers
no_outliers_map <- create_bivariate_map(
data = outlier_data %>%
filter(!pop_outlier & !income_outlier),
var_1 = "B01003_001",
var_2 = "B19013_001"
)For more information about bivariate choropleth maps:
The development of this package was funded by Grant 2020-R2-CX-0027 from the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice.